Photon-counting CT (PCCT) is a modern technology that achieves higher resolution and lower radiation dose using CdTe (Cadmium Telluride) detectors, which directly convert X-ray photons into electrical signals, unlike conventional CT systems with silicon-based indirect conversion.
At Siemens Healthineers, CdTe detectors are made from processed wafers and inspected using infrared (IR) transmission imaging. A technician uses a software tool to manually label defects such as cracks, tellurium inclusions, grain boundaries, and patterns like “Milky Way” or “Twins.” This inspection process is time-consuming, subjective, and not scalable for high-volume production.
Deep learning models such as ResNet and YOLO [1] have demonstrated strong wafer defect classification and localization performance, particularly on synthetic datasets like WM-811K [2]. However, these methods are primarily designed for structured layouts and silicon wafers, which differ significantly from the CdTe-based wafers used in photon-counting CT detectors. While studies such as Kirschenmann et al. [3] have applied deep learning to CdTe crystals using IR microscopy, their focus has been on crystal characterization rather than surface defect detection in a production setting, which is the focus of this thesis.
To the best of our knowledge, no existing method addresses the automatic classification of wafer surface defects using IR images from real CdTe wafer production, which is the focus of this thesis. The aim is to develop a deep learning model to automatically detect and classify surface defects in IR images of CdTe wafers for faster, more consistent, and scalable inspection. The dataset consists of high-resolution IR images with pixel-wise labeled masks, where defects are annotated using color codes. Each defect class includes at least 1,000 labeled images, covering types such as cracks, tellurium inclusions, and other surface anomalies.
The key steps involved in this work are:
- Literature Review A review of deep learning approaches for wafer defect detection will be conducted, with a focus on models that combine classification and localization, such as the ResNet- and YOLO-based framework proposed by Shinde et al. [2]. Relevant work on IR imaging and neural network applications for CdTe materials will also be considered.
- Model Design and Implementation A deep learning architecture will be developed to classify and potentially localize surface defects. Preprocessing steps will be designed based on the format and structure of Siemens’ internal dataset.
- Evaluation The model will be tested using standard evaluation metrics and compared to known methods. The goal is to assess how well it performs and whether it can be useful in real production environments.
References
[1] Shinde, M. et al. (2023). Wafer Defect Localization and Classification Using Deep Learning Techniques.
[2] WM-811K Dataset. MIT Lincoln Laboratory: Wafer Map Defect Dataset. https://www.ll.mit.edu/r-d/datasets/wm-811k-wafer-map-defect-dataset
[3] Kirschenmann, D. et al. (2023). Employing infrared microscopy in combination with a pre-trained neural network to visualize and analyze the defect distribution in cadmium telluride crystals.